Atmósfera (Journal)
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Development of a CFD model to simulate the dispersion of atmospheric NH3 in a semi-open barn
Get PDFThe dispersion of atmospheric NH3 constitutes the most common passive polluting system in cattle stables. Although the studies carried out through computational fluid dynamics (CFD) are focused on improving the environmental conditions in situ to increase the productivity of stables, they do not refer to determining the environmental impact of NH3 emissions into the atmosphere. This work evaluated the distribution of the NH3 flux inside a barn using CFD and its relationship with environmental conditions through probabilistic analysis by the K2 algorithm. The initial data on environmental conditions were wind speed and direction, maximum temperature, and humidity from the nearest weather station in a region characterized by hot weather conditions during summer. The vertical trajectory was used to analyze the impact of long-range transport on the spatial distribution, where 75% is between 0 and 5 m in height, and 25% is between 10 and 20 m outside the eaves. The work concluded that temperature is the main parameter of influence
Agroclimatic zoning of the state of Nayarit, México
Get PDFAgriculture productivity in the state of Nayarit has decreased since 1998. The aim of the study was to undertake the agroclimatic zoning across the state in order to determine the type of crops more convenient to render the highest yields, based on Papadakis climate classification system. Hydric and thermal characteristics pertaining to the geographic distribution of crops were used, as well as indexes derived from meteorological data provided by 25 climate stations. There were three climatic groups identified: tropical, subtropical and cold land, having four, three and two subgroups each, respectively. First two climatic groups support winter cereals such as oat (Avena sativa L.), barley (Hordeum vulgare L.), rye (Secale cereale L.) and wheat (Triticum aestivum L.); and summer cereals such as corn (Zea mays L.), millet (Panicum italicum L.), rice (Oryza sativa L.) and sorghum (Sorghum bicolor (L.) Moench); in addition to banana (Musa paradisiaca L.), citrus and potato (Solanum tuberosum L.) and sugar cane (Saccharum officinarum L.). On the other hand, corn and potato were found in the cold land climatic group. Based on Papadakis’ methodology, for each climatic sub-group identified, a set of recommendation management were given to improve yields: crop type, sowing season, irrigation, fertilizing and other agrochemicals application; and to avoid crop damage. Agroclimatic zoning map was generated by using the inverse distance weighted interpolation method. This study may contribute to the successful planning of crops across the region and thus improving the state’s economy
Air pollution and mobility in the Mexico City Metropolitan Area in times of COVID-19
Get PDFThis paper analyzes the relation between COVID-19, air pollution, and public transport mobility in the Mexico City Metropolitan Area (MCMA). We test if the restrictions to economic activity introduced to mitigate the spread of COVID-19 are associated with a structural change in air pollution levels and public transport mobility. Our results show that mobility in public transportation was significantly reduced following the government’s recommendations. Nonetheless, we show that the reduction in mobility was not accompanied by a reduction in air pollution. Furthermore, Granger-causality tests show that the precedence relation between public transport mobility and air pollution disappeared as a product of the restrictions. Thus, our results suggest that air pollution in the MCMA seems primarily driven by industry and private car usage. In this regard, the government should redouble its efforts to develop policies to reduce industrial pollution and private car usage
Comparative analysis of estimated solar radiation with different learning methods and empirical models
Get PDFSolar radiation, which is used in hydrological and agricultural modeling, agricultural, solar energy systems, and climatological studies, is the most important element of the energy reaching the earth. The present study compared the performance of two empirical equations -Angstrom and Hargreaves-Samani equations- and three machine learning models -Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM)-. Various learning models were developed for the variables used in each empirical equation. In the present study, monthly data of six stations in Turkey, three stations receiving the most solar radiation and three stations receiving the lowest solar radiation, were used. In terms of the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and determination coefficient (R2) values of each model, the LSTM was the most successful model, followed by ANN and SVM. The MAE value was 2.65 with the Hargreaves-Samani equation and decreased to 0.987 with the LSTM model, while MAE was 1.24 in the Angstrom equation and decreased to 0.747 with the LSTM model. The study revealed that the deep learning model is more appropriate to use than the empirical equations, even in cases with limited data
Effects of climate change on the potential distribution of a dominant, widely distributed oak species, Quercus candicans, in Mexico
Get PDFMexican temperate forests are among the most biodiverse in the world. At present, they face anthropogenic pressures and climatic changes. Quercus candicans is a canopy-dominant, widely distributed species common in the moist habitats of these ecosystems. Its ecological importance, habitat vulnerability, and wide distribution make it a useful model of the vulnerability of Mexican tree forest species to climate change. We used ecological niche modeling to estimate future climatic suitability for this species and its potential range shifts under two emissions scenarios and three-time frames. We also identified areas where novel climates could arise and where predictions should be interpreted cautiously. Additionally, we analyzed how climatic suitability could change across the national protected areas system. In both emissions scenarios, areas with suitable climatic conditions were predicted to experience a net reduction of more than 40% by 2070. This corresponds to more than 100 000 km2 becoming climatically unsuitable. In the national protected areas, we forecast a contraction of approximately 30%. Climatic novelty increased considerably in the higher emissions scenario (RCP 8.5), accounting for 10% of the Mexican temperate mountains, compared to 1% on RCP 4.5. Areas of expansion of suitability not intersected by novel climates occur in areas highly affected by land-use change and other anthropogenic pressures. Effective protection of temperate forests’ tree species such as Q. candicans would need to allow migrations across altitudinal gradients, as areas of stability and expansion of climatic suitability are forecasted to occur at higher altitude sections of mountain ranges
Drought Potential in Borneo Based on the RCP 4.5 Scenario
Get PDFOne of the impacts of climate change is an increase in the frequency and intensity of hydrometeorological disasters such as prolonged droughts. Borneo is one of the areas threatened by drought due to climate change. Therefore, it is important to identify and implement appropriate mitigation and adaptation measures. This study used the dynamical downscaling method by the Conformal Cubic Atmospheric Model (CCAM) to evaluate the potential for drought events in Borneo based on the RCP 4.5 scenario. The annual rainfall in Borneo for 2021-2050 is projected to increase compared to 1991-2020. However, the increase in annual rainfall does not free Borneo from the possibility of drought events in the future. This study’s results indicate that areas in southern Borneo, such as Banjarmasin, Pangkalan Bun, and Pontianak, will have a higher frequency of meteorological drought events and are also expected to experience longer periods of consecutive dry days between 2021-2050 compared to 1991-2020
Development of high-resolution annual climate surfaces for Turkey using ANUSPLIN and comparison with other methods
Get PDFMany climate models have been developed due to the importance of the effects of climatic factors on the physical and biological environment, e.g., rock weathering, species distribution, and growth patterns of plants. Accurate, reliable climate surfaces are necessary, especially for countries such as Turkey, which has a complex terrain and limited monitoring stations. The accuracy of these models mainly depends on the spatial modeling methods used. In this study, the Australian National University Spline (ANUSPLIN) model was used to develop climate surfaces and was compared with other methods such as inverse distance weighting, co-kriging, lapse rate, and multilinear regression. The results from the developed climate surfaces were validated using three methods: (1) diagnostic statistics from the surface fitting model, such as signal, mean, root mean square predictive error, root mean square error estimate, root mean square residual of the spline, and estimate of the standard deviation of the noise in the spline; (2) a comparison of error statistics between interpolated surfaces and the withheld climate data from 81 stations; and (3) a comparison with other interpolation methods using model performance metrics, such as mean absolute error, mean error, root mean square error, and R2adj. The most accurate results were obtained by the ANUSPLIN model. It explained 95, 88, 92, and 71% of the variance in annual mean, minimum and maximum temperature, and total precipitation, respectively. The mean absolute error of these models was 0.63, 1.16, and 0.72 ºC, as well as 54.82 mm. The generated climate surfaces, having a spatial resolution of 0.005º × 0.005º could contribute to the fields of forestry, agriculture, and hydrology
Urban PM2.5 concentrations in a small Colombian city and the impact associated with particle emissions generated by small-scale lime production
Get PDFThe stricter guidelines for fine particles PM2.5 recently published by the World Health Organization also motivate smaller cities to assess the exposure levels. In this study, PM2.5 was assessed in the municipality of Vijes, an important lime production center in the Cauca River Valley, Colombia. The main objective was to determine PM2.5 concentration levels in the urban background of the city and to estimate the contribution from industrial sources located west of the urbanized area. The assessment of PM2.5 concentrations in a city without fixed air quality monitors, meteorological stations, and information on emission sources, was designed to be expedient and possible to perform with a very restricted budget. Four low-cost optical sensors and one low-cost meteorological station were installed during two separate campaigns, each three to four months long. The PM2.5 measurements were analyzed with the support of meteorological data and dispersion modeling. Mean levels of PM2.5 in the urban background were found to be below the Colombian limit value of 25 µg m–3, in the range of 14 to 19 µg m–3, and with lower levels in the city center. The monitor located in the westernmost urban area, closest to the industrial plants, registered a high 24-h mean level close to the national limit value. The industrial contribution to long-term PM2.5 concentrations in the urban background of Vijes was estimated to be within a maximum of 6 µg m–3, i.e., a minor fraction of the monitored PM2.5 mean levels in the urban background. The dominating part of the PM2.5 concentrations could be attributed to other anthropogenic sources within or east of Vijes, as well as originating from the regional background concentration characterizing the Cauca River Valley to the east of Vijes, where pre-harvest sugar cane burning is common
Modeling tropical storm Elsa: Flood map simulation using multisensory precipitation in Connecticut
Get PDFA flood map simulation in the Fenton River watershed, Connecticut, was conducted for Tropical Storm Elsa occurred in early July 2021, using Multi Radar Multi Sensor-Quantitative Precipitation Estimation (MRMS-QPE) as input to force the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) to simulate discharges in the mainstream of the watershed. The simulated discharges were calibrated using observed discharges at the Old Turnpike Bridge USGS station, and they were used to force a Hydrologic Engineering Center-River Analysis System (HEC-RAS) 2D model of the Fenton River watershed. The simulated stages were calibrated using observed stages at Old Turnpike Bridge USGS station to simulate flood maps in the mainstream of the watershed. The resulting use of HEC-HMS and HEC-RAS 2D models coupled with MRMS-QPE precipitation shows that these models set up is user-friendly. The model shows stability and the capacity to simulate flood maps along the whole mainstream of the Fenton River with good accuracy
Patterns related to pollutant concentrations in the Metropolitan Area of Belo Horizonte, Brazil
Get PDFAir pollution from human and industrial activities has been a major concern in recent years. Among the various pollutants found in the atmosphere, particulate matter (PM) and ozone (O3) show significant occurrences, with high concentrations in urban centers frequently associated with environmental and public health problems. Therefore, this study uses the analysis of variance (ANOVA) technique and Tukey’s test to investigate patterns related to the variability of maximum daily O3 concentrations and mean daily concentrations of PM with a diameter less than 10 μm (PM10), registered between 2007 and 2012 through six sites in the Metropolitan Area of Belo Horizonte, Brazil. To this end, the data were analyzed using ANOVA arranged in a factorial scheme (6 × 4 × 2) with four repetitions per treatment, followed by Tukey’s test. In the ANOVA and Tukey’s test, the first factor (A) represents the six air quality monitoring stations, the second (B) represents the seasons, and the third (C), the measurements carried out during working days and weekends. Seasonal variability patterns show higher concentrations of O3 in spring and of PM10 in winter. The values were 22.9 and 35.32% higher than the annual averages of O3 and PM10 concentrations, respectively. The mean values for working days and weekends showed different patterns for the two pollutants. PM10 concentrations were 11% higher during working days when compared to weekends. The O3 weekend effect was found only in one of the stations. The profiles of vehicular and industrial emissions have been identified as a potential factor leading to these results